Drivers of cloud droplet number variability in the ... · 1 1 Drivers of cloud droplet number...

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Drivers of cloud droplet number variability in the summertime 1

Southeast United States 2

Aikaterini Bougiatioti1,2, Athanasios Nenes2,3,4, Jack J. Lin2,a, Charles A. Brock5, Joost A. de Gouw5,6,b, Jin 3 Liao5,6,c,d, Ann M. Middlebrook5, André Welti5,6,e 4

1Institute for Environmental Research & Sustainable Development, National Observatory of Athens, P. 5 Penteli, GR-15236, Greece 6

2School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA 7 3Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil & Environmental 8

Engineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland 9 4Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, GR-10

26504, Greece 11 5Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, 80305, USA 12 6Cooperative Institute for Research in Environmental Sciences, Univ. of Colorado, Boulder, CO, 80309, 13

USA 14 a now at: Nano and Molecular Systems Research Unit, Box 3000, FI-90014 University of Oulu, Oulu, 15

Finland 16 b now at: Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO, USA 17 c now at: Atmospheric Chemistry and Dynamic Laboratory, NASA Goddard Space Flight Center, 18

Greenbelt, MD, USA 19 d now at: Universities Space Research Association, GESTAR, Columbia, MD, USA 20 e now at: Atmospheric Composition Research Unit, Finnish Meteorological Institute, Helsinki, Finland 21

Correspondence to: Aikaterini Bougiatioti ([email protected]), Athanasios Nenes 22 ([email protected]). 23

Abstract 24

The Southeast United States has experienced a different climate warming trend compared to other places 25

worldwide. Several hypotheses have been proposed to explain this trend, one being the interaction of 26

anthropogenic and biogenic aerosol precursors that synergistically promote aerosol formation, elevate cloud 27

droplet concentration and induce regional cooling. We examine these aerosol-cloud droplet links by 28

analyzing regional scale data collected onboard the NOAA WP-3D aircraft during the 2013 Southeast 29

Nexus (SENEX) campaign to quantify the sensitivity of droplet number to aerosol number, chemical 30

composition and vertical velocity on a regional scale. The observed aerosol size distributions, chemical 31

composition and vertical velocity distribution (Gaussian with standard deviation σw) are introduced into a 32

state-of-the-art cloud droplet parameterization to show that cloud maximum supersaturations in the region 33

are low, ranging from 0.02 to 0.52% with an average of 0.14±0.05%. Based on these low values of 34

supersaturation, the majority of activated droplets correspond to particles of diameter 90 nm and above. 35

Droplet number shows little sensitivity to total aerosol owing to their strong competition for water vapor. 36

Given, however, that σw exhibits considerable diurnal variability (ranging from 0.16 m s-1 during nighttime 37

to over 1.2 m s-1 during day), its covariance with total aerosol number (Na) during the same period amplifies 38

https://doi.org/10.5194/acp-2020-225Preprint. Discussion started: 17 March 2020c© Author(s) 2020. CC BY 4.0 License.

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predicted response in cloud droplet number (Nd) by 3 to 5 times. Therefore, correct consideration of vertical 39

velocity and its covariance with time and aerosol amount is important for fully understanding aerosol-cloud 40

interactions and the magnitude of the aerosol indirect effect. Datasets and analysis such as the one presented 41

here can provide the required constraints for addressing this important problem. 42

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1. Introduction 44

Atmospheric particles (aerosols) interact with the incoming solar radiation through scattering and 45

absorption processes which tend to cool the Earth, especially over dark surfaces such as oceans and forests 46

(Brock et al., 2016a). Aerosols also act as cloud condensation nuclei (CCN) and subsequently form cloud 47

droplets and indirectly affect climate through modification of cloud radiative properties - an effect which 48

constitutes one of the most uncertain aspects of anthropogenic climate change (Seinfeld et al., 2016). 49

Studies often highlight the importance of constraining the aerosol size distribution, particle composition 50

and mixing state for predicting CCN concentrations (Cubison et al., 2008; Quinn et al., 2008). Model 51

assumptions often cannot consider the full complexity required to comprehensively compute CCN – which 52

together with other emissions and process uncertainties lead to CCN prediction errors that can be significant 53

(e.g., Fanourgakis et al., 2019). Owing to the sublinear response of cloud droplet number concentration (Nd) 54

to aerosol perturbations, prediction errors in CCN generally result in errors in Nd which are less than those 55

for CCN (Fanourgakis et al., 2019). The sublinear response arises because elevated CCN concentration 56

generally increases the competition of the potential droplets for water vapor; this in turn depletes 57

supersaturation and the Nd that can eventually form (Reutter et al., 2009; Bougiatioti et al., 2016; 58

Fanourgakis et al., 2019; Kalkavouras et al., 2019). A critically important parameter is the vertical velocity; 59

so important in fact that droplet number variability may be driven primarily by vertical velocity variations 60

(Kacarab et al., 2020; Sullivan et al., 2019). Compared to aerosols, vertical velocity is much less observed, 61

constrained and evaluated in aerosol-cloud interaction studies, hence may be a source of persistent biases 62

in models (Sullivan et al., 2019). 63

The Southeast United States (SEUS) presents a particularly interesting location for studying regional 64

climate change, as it has not considerably warmed over the past 100 years – except for the last decade 65

(Carlton et al., 2018; Yu et al., 2014; Leibensperger et al., 2012b). These trends are in contrast with the 66

trends observed in most locations globally (IPCC 2013), and several hypotheses have been proposed to 67

explain this regional phenomenon, including the effect of involving short-lived climate forcers such as 68

secondary aerosols combined with the enhanced humidity in the region and their impact on clouds (Carlton 69

et al., 2018; Yu et al., 2014). Here, we analyze data collected during the Southeast Nexus of Air Quality 70


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and Climate (SENEX) campaign in June-July 2013, which was the airborne component led by the National 71

Oceanic and Atmospheric Administration (NOAA), of a greater measurement campaign throughout the 72

SEUS, the Southeast Atmosphere Study (SAS; Carlton et al., 2018). Here we analyze data collected onboard 73

the NOAA WP-3D and apply a state-of-the-art droplet parameterization to determine the maximum 74

supersaturation and Nd achieved in cloudy updrafts, for all science flights with available number size 75

distribution and chemical composition data. We also determine the sensitivity of droplet formation to 76

vertical velocity and aerosol, with the purpose of understanding the drivers of droplet variability in the 77

boundary layer of the SEUS by obtaining regional-scale, representative values of the relationship between 78

the driving parameters and cloud droplet number. 79

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2. Methods 81

2.1 Aircraft instrumentation 82

The analysis utilizes airborne, in situ data collected during the June-July 2013 SENEX mission, aboard the 83

National Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft (typical airspeed ~100 m s-1) 84

based in Smyrna, Tennessee (36o00’32’’N, 86o31’12’’W). In total, twenty research flights were conducted. 85

Based on the availability of the relevant data described below, thirteen flights are analyzed in this work. 86

Description of the analyzed research flights are provided in Table 1. Detailed information on the 87

instrumentation and measurement strategy during the SENEX campaign can be found in Warneke et al. 88

(2016). 89

Dry particle number distributions from 4 - 7000 nm were measured using multiple condensation and optical 90

particle counters. 4-700 nm particles were measured by a nucleation mode aerosol size spectrometer 91

(NMASS; Warneke et al., 2016) and an ultra-high sensitivity aerosol spectrometer (UHSA; Brock et al., 92

2011), while for larger particles with dry diameters between 0.7 and 7.0 μm, a custom-built white-light 93

optical particle counter (WLOPC) was used. 94

Measurements of the composition of submicron vacuum aerodynamic diameter non-refractory aerosol (less 95

than 0.7 μm diameter) were made with a Compact Time-of-Flight Aerosol Mass Spectrometer (C-ToF-96

AMS; Aerodyne, Billerica, Massachesetts, US) (Canagaratna et al., 2007; Kupc et al., 2018) customized 97

for aircraft use, with a 10 s time resolution (Warneke et al., 2016). Particles entering the instrument are 98

focused and impacted on a 600 oC inverted-cone vaporizer. The volatilized vapors are analyzed by electron 99

ionization mass spectrometry, providing mass loadings of sulfate, nitrate, organics, ammonium and 100

chloride. For the C-ToF-AMS, the transmission efficiency of particles between 100 and 700 nm is assumed 101

to be 100% through the specific aerodynamic focusing lens used while mass concentrations are calculated 102


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using a chemical composition-dependent collection efficiency (Middlebrook et al., 2012; Wagner et al., 103

2015). The C-ToF-AMS only measures non-refractory aerosol chemical composition, therefore this 104

analysis provides mass loadings of sulfate, nitrate, ammonium and organic constituents with a 10 s time 105

resolution and neglects the contribution of black carbon (BC). The calculation of the average volume 106

fractions from the mass loading follows that of Moore et al. (2012). An average organic density of 1.4 g 107

cm-3 is used, characteristic of aged aerosol (Moore et al., 2011; Lathem et al., 2013) while for the inorganic 108

species the respective densities are used, assuming the aerosol to be internally mixed. 109

The aircraft was equipped by the NOAA Aircraft Operations Center (AOC) flight facility, incorporating a 110

suite of instruments to provide information on exact aircraft position as well as numerous meteorological 111

parameters (Warneke et al., 2016). The analysis in this work makes use of vertical wind velocity, aircraft 112

radar altitude, and ambient temperature, pressure and relative humidity (RH) provided by NOAA AOC. 113

Location of the instrumentation on the aircraft can be found elsewhere (Warneke et al., 2016). For 114

measurements inside the fuselage a low turbulence inlet (Wilson et al., 2004) and sampling system (Brock 115

et al., 2011; 2016a) was used to decelerate the sample flow to the instruments. The C-ToF-AMS was 116

connected downstream of an impactor with 50% efficiency at a 1.0 μm aerodynamic diameter (PM1) cut-117

point (Warneke et al., 2016). 118

2.2 Aerosol hygroscopicity parameter 119

The aerosol hygroscopicity parameter (Petters and Kreidenweis, 2007), κ, is calculated assuming a mixture 120

of an organic and inorganic component with volume fraction εorg, εinorg and characteristic hygroscopicity 121

κorg, κinorg, respectively (κ=εinorgκinorg+εorgκorg). The organic and inorganic volume fraction are derived from 122

the C-ToF-AMS data. Since throughout the summertime SEUS, aerosol inorganic nitrate mass and volume 123

fraction are very low (Weber et al., 2016; Fry et al., 2018), κinorg =0.6, representative for ammonium sulfate, 124

is used. For the organic fraction, a hygroscopicity value of κorg=0.14 is used, based on concurrent 125

measurements conducted at the ground site of the SAS at the rural site of Centreville, Alabama (Cerully et 126

al., 2015). This value is also in accordance with the cumulative result of studies conducted in the Southeast 127

US using measurements of droplet activation diameters in subsaturated regimes, providing κorg of > 0.1 128

(Brock et al., 2016a). 129

2.3 Cloud droplet number and maximum supersaturation 130

Using the observed aerosol number size distribution (1 s time resolution) and the hygroscopicity derived 131

from the chemical composition measurements (10 s time resolution), we calculate the droplet number (Nd) 132

and maximum supersaturation (Smax) that would form in clouds in the airmasses sampled. Droplet number 133


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and maximum supersaturation calculations are carried out at a regional scale using an approach similar to 134

that of Bougiatioti et al. (2016) and Kalkavouras et al. (2019) with the sectional parameterization of Nenes 135

and Seinfeld (2003), later improved by Barahona et al. (2010) and Morales Betancourt and Nenes (2014a). 136

A sectional representation of the size distribution is used and provided for each data point of each flight 137

(per second, e.g. for Flight 5, n=23213 data points). Given that chemical composition is provided with a 10 138

s time resolution, the same hygroscopicity values are used for 10 size distributions during each flight. 139

Temperature and pressure required for droplet number calculations are obtained from the NOAA AOC 140

flight facility dataset. 141

Droplets form from activation of aerosol in cloudy updrafts, so here we use the available measurements of 142

vertical velocity together with the aerosol measurements to derive a potential cloud droplet concentration. 143

Given that vertical velocity varies considerably inside the boundary layer we represent droplet number with 144

the average concentration that results from integrating over the distribution (probability density function, 145

PDF) of observed updraft velocities. To accomplish this, each flight is divided in segments where the 146

aircraft flew at a constant height. For each segment, the positive vertical velocities are fit to a Gaussian 147

distribution with mean of zero and width of spectral dispersion σw. Positive vertical velocities (“updrafts”) 148

were used, as they are the part of the vertical velocity spectrum that is responsible for cloud droplet 149

formation. The σw values derived from the level leg segments are then averaged into one single σw value 150

(and standard deviation) to represent the flight. Application of the “characteristic velocity” approach 151

(Morales and Nenes, 2010) then gives the PDF-averaged droplet number concentration by calling the 152

droplet parameterization at a single “characteristic” velocity, w*=0.79σw (Morales and Nenes, 2010). This 153

calculation approach is applied to each size distribution measured. Apart from its theoretical basis, this 154

methodology has shown to provide closure with observed droplet numbers in ambient clouds (e.g. Kacarab 155

et al., 2020). 156

In determining σw, we consider segments that are expected to be in the boundary layer: 91 % of the segments 157

are below 1000 m (mean altitude ~700 m; Table 2) typically corresponding to the height of the boundary 158

layer in the summertime US (Seidel et al., 2013). The vertical velocity distributions observed gave σw 159

=0.97±0.21 m s-1 for daytime flights, and σw =0.23±0.04 m s-1 for nighttime flights (Table 2). Because of 160

this strong diurnal variation in σw, potential droplet formation is evaluated at four vertical velocities that 161

cover the observed ranged, namely 0.1, 0.3, 0.6 and 1 m s-1. 162

We also compute the sensitivity of the derived Nd, to changes in aerosol number concentration (Na), κ and 163

σw, expressed by the partial derivatives Nd/Na, Nd/κ and Nd/σw computed from the parameterization 164

using a finite difference approximation (Bougiatioti et al., 2016; Kalkavouras et al., 2019). These 165

sensitivities, together with the observed variance in Na, κ, and σw are also used to attribute droplet number 166


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variability to variations in the respective aerosol and vertical velocity parameters following the approach 167

of Bougiatioti et al. (2016) and Kalkavouras et al. (2019). 168

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3. Results and Discussion 170

3.1. Particle composition and size distribution 171

For the determination of the different aerosol species present, neutral and acidic sulfate species are 172

distinguished by the molar ratio of ammonium to sulfate ions. A molar ratio higher than 2 indicates the 173

presence of only ammonium sulfate, while values between 1 and 2 indicate the presence of both ammonium 174

sulfate and bisulfate (Seinfeld and Pandis, 1998). For most of the flights, the molar ratio of ammonium 175

versus sulfate was well above 2, having a mean value of 2.41±0.72 (median 2.06). For the nighttime flights 176

the values were somewhat lower (1.91±0.42 and median of 1.85, respectively). Nevertheless, ammonium 177

sulfate is always the predominant sulfate salt. Organic mass fractions for the SENEX research flights are 178

provided in Table 1. Overall, organic aerosol was found to dominate during all flights, contributing 66%-179

75% of the total aerosol volume. Most of the remaining aerosol volume consists of ammonium sulfate, 180

ranging from 12%-39% (with a mean of 23%±6%). The organic mass fraction during the flights was found 181

to decrease with height (see Fig. 1). This vertical variability of the chemical composition can have a strong 182

impact on droplet number within the boundary layer, as air masses from aloft may descend and interact 183

with that underneath. Figure 1 represents the organic mass fractions during Flights 5 and 12. The lowest 184

organic mass fractions overall were observed during Flight 12 (35%±18% with values < 5% for altitudes 185

>3000 m, Fig.1b) while the highest ones were observed at flights over predominantly rural areas (Flights 5 186

(Fig. 1c) , 10 and 16). During Flight 5 the organic mass fraction was high (68%±5%), with the highest 187

values found in the free troposphere at altitudes > 3000 m. High organic mass fractions were also found 188

during a nighttime flight that included portions of the Atlanta metropolitan area, with values up to 78%. 189

The impact of the chemical variability on droplet number is discussed in section 3.2. 190

The predominance of the organic fraction is also reflected in the hygroscopicity parameter values, with an 191

overall κ = 0.25±0.05, which is close to the proposed global average of 0.3 (Seinfeld and Pandis, 1998). 192

The highest values are, as expected, for flights exhibiting the lowest organic mass fraction, namely Flight 193

12 with a κ = 0.39 (Table 2). The rest of the κ-values are close to the overall values, as the organic mass 194

fractions are around 0.65. 195

Median aerosol size distributions are obtained from the median and interquartile range in each size bin from 196

the aerosol size distribution measurements during segments where the aircraft flew at a constant height. 197

The impact of the variability of the total aerosol number on droplet number is discussed in detail further in 198


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section 3.2. Overall, number concentrations ranged from around 500 to over 100000 cm-3 with number size 199

distributions varying markedly over the course of a flight. In general, free tropospheric distributions 200

exhibited characteristics of a bimodal distribution with a prominent broad accumulation mode peak (80-201

200 nm) and an Aitken mode peak (30-60 nm) (Fig. 2a) while boundary layer size distributions exhibited a 202

more prominent accumulation mode (Fig. 2b). There was considerable variability in the contributions of 203

the nucleation, Aitken, and accumulation modes to the total aerosol number, depending on altitude and 204

proximity to aerosol sources (Fig. 2c). Nevertheless, the modal diameters did not vary much. Distributions 205

during nighttime flights exhibited similar total aerosol number and variability; nevertheless, size 206

distributions were more complex exhibiting even three different modes (20-40, 70-100 and 130-200 nm; 207

Fig. 2d). Considering that mostly particles in the accumulation mode activate into cloud droplets (particles 208

with diameters >90 nm), contrasts between day and nighttime aerosol characteristics/variability may not be 209

as large, and driven primarily by the total aerosol number in the accumulation mode. 210

3.2 Potential cloud droplet number 211

The calculation of Nd and Smax, was carried out for all thirteen research flights. Results are given in Tab. 3 212

for the four different values (0.1, 0.3, 0.6 and 1 m s-1) of σw. Overall it can be seen that for all flight 213

conditions and for low σw, Nd shows a low variance (mean of 132±20 for 0.1 m s-1 and 350±100 for 0.3 m 214

s-1). For a given σw, the variance of Nd is predominantly attributed to relative changes in Na rather than 215

changes in the chemical composition (expressed by changes in the hygroscopicity parameter, κ). The 216

highest relative contribution of the chemical composition (12% and 35% for 0.1 and 0.3 m s-1, respectively) 217

to the variation of Nd is found for Flight 18, during which the total aerosol number was the lowest. Indicative 218

of a “cleaner” environment; the organic mass fraction was relatively lower and the hygroscopicity 219

parameter was higher. Even though the lowest organics mass fraction and highest κ were observed during 220

Flight 12, droplet formation is much more sensitive to changes in aerosol concentration than to variations 221

in composition. 222

As the vertical velocity increases, so does supersaturation and consequently the droplet number (by 62% 223

from 0.1 to 0.3 m s-1, 70% from 0.3 to 0.6 m s-1 and another 39% from 0.6 to 1 m s-1). The relative 224

contribution of the chemical composition to the variation of cloud droplet number increases from 5±3% for 225

0.1 m s-1, to 12.3±8% for 0.3 m s-1, to 14.5±10% for 0.3 m s-1 and 16.5±9% for 1 m s-1. The highest droplet 226

numbers are estimated for Flights 6 and 10, which included urban environments during daytime (Atlanta). 227

Overall during daytime, when σw varies little and is large, and Na is high, the relative contribution of Na to 228

the variation of Nd is the highest (more than 90%) while the relative contribution of κ is limited (less than 229

10%) (see Table 3, Flights 10, 11, 12, 17 and 19). Turbulence is limited during nighttime when σw is the 230


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lowest (0.23±0.04); therefore, the σw = 0.3 m s-1 case is most representative of nighttime conditions. During 231

daytime, when σw is high (0.97±0.21), σw = 1 m s-1 should be considered as most representative. 232

As σw varies considerably throughout the day, we estimate its contribution together with variations in Na 233

and κ, to the total variability in Nd based on Nd/κ, Nd/Na and Nd/σw and the variances of κ, Na and σw 234

(Table 4). The σw variation during nighttime, although small (always less than 10%), consistently remains 235

an important contributor to Nd variability, because droplet formation tends to be in the updraft velocity-236

limited regime. At higher values of σw (Table 4), the contribution of σw variability to Nd variability is reduced 237

and dominated by Na variability. 238

To explore the importance of aerosol compared to updraft velocity, we focus on two pairs of flights 239

conducted in two sectors, from each sector one during day- and one during night-time (see Fig.3). In both 240

pairs of flights (Flight 5 and 15, and Flight 6 and 9), σw varies about the same between night and day (Table 241

4). For the first pair of flights, the daytime variability in Nd (which is 69%) is to within 75% driven by 242

aerosol (69% by Na and 7% from κ) and 24% by σw. For nighttime, 58% by aerosol (51% by Na and 7% 243

from κ) and 42% of the variability is driven by σw. For the second pair of night/day flights, Na is on average 244

similar, σw varies by a factor of 4.0 and κ varies by 13%. Attribution calculations suggest that the diurnal 245

variability in Nd (where daytime values are 72.1% higher than nighttime) is 3, 54 and 43% for κ, Na and σw, 246

respectively during day and 7, 76, and 17% driven by κ, Na and σw, respectively during night (Table 4). In 247

the second sector, 57% of the variability in Nd is driven by aerosol during the day and 83% during the night. 248

As expected, droplet number (Nd) and maximum supersaturation (Smax) increases as σw becomes larger. The 249

highest Smax are around 0.2-0.3% and found for flights which exhibited large and highly variable σw (Flights 250

4, 5, 12 and 19) while the lowest Smax are around 0.10% and found for the nighttime flights (Flights 9, 15 251

and 16). All other flights yield similar Smax, which are around 0.13%. Based on the calculated Smax for every 252

flight, the majority of the activated droplets correspond to particles of 90 nm diameter and above. Figure 3 253

presents the calculated Nd for the four aforementioned flights, namely Fights 5 (Fig. 3a), 15 (Fig. 3b), 6 254

(Fig. 3c) and 9 (Fig. 3d) using the observed σw. The size of the markers represents the number of droplets, 255

while the color scale the respective total aerosol number. 256

Figure 4 shows Nd relative to Na for flights conducted in two sectors, during day and night (Flights 5 & 15, 257

and Flights 6 & 9, respectively). It can be seen that throughout these flights, Nd reaches a plateau, where 258

any additional aerosol does not translate to any significant increase in Nd. This plateau is caused by strong 259

water vapor limitations and is different for day and night. Smax is lower during night because vertical wind 260

velocity, ambient T and RH are lower. The same factors cause that for Flight 6 & 9 (Fig. 4c & d) where Na 261

was almost the same, Nd is almost 3.5 times lower during night (Flight 9). For the whole dataset (13 flights), 262


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results are summarized in Figure 5, where droplet numbers are calculated based on the observed σw and the 263

respective “characteristic”, mean velocities, w*. Under low w* conditions, Na variability does not result in 264

an important change in Nd. On the contrary, when w* tends to increase and Na increases, as is characteristic 265

of polluted regions, during daytime, then the impact on droplet number is more notable. This point is evident 266

in Figure 6, comprising the different segments of the flights when the aircraft sampled at practically the 267

same altitude within the boundary layer. It can be seen that Na is enhanced as w* increases. The lowest w* 268

values (shaded area) correspond to the segments of the flights during nighttime. 269

Overall, Smax of clouds from all the evaluated SENEX data, is 0.14±0.05%. Tripling σw from 0.1 to 0.3 m s-270

1 results in 31% increase in Smax, while doubling from 0.3 to 0.6 m s-1 results in 26.2% increase in Smax and 271

a further σw increase to 1 m s-1 leads to an additional 20.7% increase in Smax. Overall effect of updraft 272

velocity on calculated Nd: tripling σw from 0.1 to 0.3 m s-1 results in a 61.9% increase in Nd, doubling from 273

0.3 to 0.6 m s-1 results in a 40.5% Nd increase; increasing σw to 1 m s-1 leads to an additional 26.9% increase 274

in Nd. Furthermore, for a given σw, despite of the presence or not of a large number of aerosol (e.g. Flight 275

10 where Na is 2.7 times higher than Na in Flight 15) the difference in calculated Nd for 0.6 m s-1 is only 1.3 276

times higher for Flight 10 than Flight 15. This highlights the relative insensitivity of Nd to variations in Na 277

for constant σw. 278

279

4. Summary and Conclusions 280

Measurements of wind velocity, ambient conditions (T, RH), aerosol number size distribution and 281

composition in the SEUS obtained during the SENEX 2013 project are used to analyze the drivers of droplet 282

formation. Overall 13 research flights are studied, covering environments over sectors with different aerosol 283

sources, impacting total aerosol number, size distribution and chemical composition. Aerosol volume is 284

largely dominated by an organic fraction resulting in a calculated hygroscopicity of 0.25±0.05. 285

Based on the calculation of cloud droplet number concentration (Nd) and maximum supersaturation (Smax), 286

we find that on a regional scale, most of the variability of Nd is due to the fluctuations in Na (Table 4), in 287

accordance with other recent studies (Fanourgakis et al., 2019). Nonetheless, Nd levels are also sensitive to 288

fluctuations in σw, as a variation by a factor of 4.0 in σw may lead to an Nd variation of almost a factor of 289

3.6 and at the same time the Nd response to different Na levels may be enhanced by a factor of 5 (Figure 4). 290

Smax changes in response to aerosol concentration, in a way that tends to partially mitigate Nd responses to 291

aerosol. Overall, maximum supersaturation levels remain quite low (0.14±0.05%) with predicted levels 292

being much lower in lower altitudes (0.05±0.1%). Because of the strong competition for water vapor 293

(expressed by the low Smax), cloud droplet number exhibits enhanced sensitivity to aerosol number 294


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variations throughout the flights, regardless of aerosol composition. On the other hand, droplet 295

concentration especially within the boundary layer approaches a “plateau” that is strongly driven by vertical 296

velocity (turbulence) and the resulting supersaturation, but also aerosol concentration. In “cleaner” 297

environments where total aerosol number is lower, the relative contribution of vertical velocity to cloud 298

droplet number is almost half during nighttime (24% vs. 42% during daytime) while the relative 299

contribution of Na to the variance in Nd is somewhat higher (69% vs. 51% during daytime) even though Na 300

is 2-fold lower during night. On the contrary, in environments with elevated concentrations of 301

accumulation-mode particles, the majority of cloud droplet number variations (54% during nighttime vs. 302

76% during daytime) can be attributed to changes in total aerosol number and to a lesser extent to vertical 303

velocity (43% during nighttime vs. 17% during daytime). The relative contribution of the total aerosol 304

number to the cloud droplet number dominates over variations in chemical composition (expressed by κ). 305

There are cases however where chemical composition variability contributes a non-negligible (~9%) 306

contribution to droplet number variability. 307

Overall, our results show that atmospheric dynamics is a key driver of cloud droplet formation and its 308

variability in the region. Especially in cases when the boundary layer turbulence is low (e.g. during 309

nighttime), low vertical velocity, generating only small supersaturation, can be as important a contributor 310

to droplet number variability as aerosol number. For cases with high vertical velocities and high aerosol 311

number concentration, it is the aerosol concentration that dominates the variability in cloud droplet number. 312

On average, the two variables (Na and σw) contribute almost equally to the variability in cloud droplet 313

number concentration (Nd) and together account for more than 90% of variability. This finding is consistent 314

with recent modeling studies noting the importance of vertical velocity variability as a driver of the temporal 315

variability of global hydrometeor concentration (Morales Betancourt and Nenes, 2014b; Sullivan et al., 316

2016). Furthermore, the Nd enhancement from changes in Na is magnified up to 5 times from concurrent 317

changes in σw. A similar situation has also been observed in smoke-influenced marine boundary layers in 318

the S.Atlantic (Kacarab et al., 2020). Altogether, these findings carry important implications for model 319

assessments of aerosol indirect climate forcing (e.g., Leibensperger et al., 2012a) and aerosol-cloud 320

interaction studies using remote sensing, as patterns of cooling (although consistent with aerosol and cloud 321

fields) may omit the covariance of vertical velocity with aerosol number, therefore neglecting this important 322

driver of hydrometeor variability. 323

Data Availability: The data used in this study can be downloaded from the NOAA public data repository 324

at https://www.esrl.noaa.gov/csd/projects/senex/. The Gaussian fits used for determining σw and the droplet 325

parameterization used for the calculations in the study are available from [email protected] upon 326

request. 327


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Author Contributions: conceptualization, A.B. and A.N.; methodology, A.B. and A.N.; software, A.N.; 328

formal analysis, A.B. and A.N.; investigation, A.B., A.N. and J.J.L.; data curation, A.B., J.J.L., C.B., J.A.G., 329

J.L., A.M.M., A.W.; writing—original draft preparation, A.B. and A.N.; writing—review and editing, A.B., 330

A.N., J.J.L., C.B., A.W., additional comments by A.M.M.; visualization, A.B. A.N. and J.J.L.; supervision, 331

A.N.; project administration, A.N.; funding acquisition, A.N. 332

Funding: This study was supported by the Environmental Protection Agency STAR Grant R835410, the 333

Action “Supporting of Postdoctoral Researchers” of the Operational Program “Education and Lifelong 334

Learning” (action’s beneficiary: General Secretariat for Research and Technology) and is co-financed by 335

the European Social Fund (ESF) and the Greek State. We also acknowledge funding from the European 336

Research Council, CoG-2016 project PyroTRACH (726165) funded by H2020-EU.1.1. – Excellent 337

Science. 338

Conflicts of Interest: The authors declare no conflict of interest. 339

340

References 341

Barahona, D., West, R.E.L., Stier, P., Romakkaniemi, S., Kokkola, H., and Nenes, A.: Comprehensively 342

accounting for the effect of giant CCN in cloud activation parameterizations, Atmos. Chem. Phys., 10, 343

2467–2473, doi:10.5194/acp-10-2467-2010, 2010. 344

Bougiatioti, A., Bezantakos, S., Stavroulas, I., Kalivitis, N., Kokkalis, P., Biskos, G., Mihalopoulos, N., 345

Papayannis, A., and Nenes, A.: Biomass-burning impact on CCN number, hygroscopicity and cloud 346

formation during summertime in the eastern Mediterranean, Atmos. Chem. Phys., 16, 7389–7409, 347

https://doi.org/10.5194/acp-16-7389-2016, 2016. 348

Bougiatioti, A., Argyrouli, A., Solomos, S., Vratolis, S., Eleftheriadis, K., Papayannis, A., and Nenes, A.: 349

CCN activity, variability and influence on droplet formation during the HygrA-Cd campaign in 350

Athens, Atmosphere, 8, 108, https://doi.org/10.3390/atmos8060108, 2017. 351

Brock, C. A., Cozic, J., Bahreini, R., Froyd, K. D., Middlebrook, A. M., McComiskey, A., Brioude, J., 352

Cooper, O. R., Stohl, A., Aikin, K. C., de Gouw, J. A., Fahey, D. W., Ferrare, R. A., Gao, R. S., Gore, 353

W., Holloway, J. S., Hübler, G., Jefferson, A., Lack, D. A., Lance, S., Moore, R. H., Murphy, D. M., 354

Nenes, A., Novelli, P. C., Nowak, J. B., Ogren, J. A., Peischl, J., Pierce, R. B., Pilewskie, P., Quinn, 355

P. K., Ryerson, T. B., Schmidt, K. S., Schwarz, J. P., Sodemann, H., Spackman, J. R., Stark, H., 356

Thomson, D. S., Thornberry, T., Veres, P., Watts, L. A., Warneke, C., and Wollny, A. G.: 357

Characteristics, sources, and transport of aerosols measured in spring 2008 during the aerosol, 358


12

radiation, and cloud processes affecting Arctic Climate (ARCPAC) Project, Atmos. Chem. Phys., 11, 359

2423–2453, 2011. 360

Brock, C. A., Wagner, N. L., Anderson, B. E., Attwood, A. R., Beyersdorf, A., Campuzano-Jost, P., 361

Carlton, A. G., Day, D. A., Diskin, G. S., Gordon, T. D., Jimenez, J. L., Lack, D. A., Liao, J., Markovic, 362

M. Z., Middlebrook, A. M., Ng, N. L., Perring, A. E., Richardson, M. S., Schwarz, J. P., Washenfelder, 363

R. A., Welti, A., Xu, L., Ziemba, L. D., and Murphy, D. M.: Aerosol optical properties in the 364

southeastern United States in summer – Part 1: Hygroscopic growth, Atmos. Chem. Phys., 16, 4987-365

5007, https://doi.org/10.5194/acp-16-4987-2016, 2016a. 366

Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra,M. R., Zhang, Q., Onasch, T. B., 367

Drewnick, F., Coe, H., Middlebrook, A., Delia, A., Williams, L. R., Trimborn, A. M., Northway, M. 368

J., DeCarlo, P. F., Kolb, C. E., Davidovits, P., and Worsnop, D. R.: Chemical and microphysical 369

characterization of ambient aerosols with the aerodyne aerosol mass spectrometer, Mass Spectrometry 370

Reviews, 26, 185–222, 2007. 371

Carlton, A.G., J.A. de Gouw, J.L. Jimenez, J.L. Ambrose, A.R. Attwood, S. Brown, K.R. Baker, C. Brock, 372

R.C. Cohen, S. Edgerton, C. Farkas, D. Farmer, A.H. Goldstein, L. Gratz, A. Guenther, S. Hunt, L. 373

Jaeglé, D.A. Jaffe, J. Mak, C. McClure, A. Nenes, T.K.V. Nguyen, J.R. Pierce, S. de Sa, N.E. Selin, 374

V. Shah, S. Shaw, P.B. Shepson, S. Song, J. Stutz, J. Surratt, B.J. Turpin, C. Warneke, R.A. 375

Washenfelder, P.O. Wennberg, and Xianling Zhou: A synthesis of the southeast atmosphere studies: 376

coordinated investigation of fundamental atmospheric chemistry questions, Bulletin of the American 377

Meteorological Society, doi: 10.1175-BAMS-D-16-0048.1, 2018. 378

Cubison, M. J., Ervens, B., Feingold, G., Docherty, K. S., Ulbrich, I. M., Shields, L., Prather, K., Hering, 379

S., and Jimenez, J. L.: The influence of chemical composition and mixing state of Los Angeles urban 380

aerosol on CCN number and cloud properties, Atmos. Chem. Phys., 8, 5649-5667, 381

https://doi.org/10.5194/acp-8-5649-2008, 2008. 382

DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, 383

K., Horvath, T., Docherty, K. S., Bates, D. R., and Jimenez, J. L.: Field-Deployable, High-Resolution, 384

Time-of-Flight Aerosol Mass Spectrometer, Anal. Chem., 78, 8281–8289, doi:8210.1021/ac061249n, 385

2006. 386

Fanourgakis, G. S., Kanakidou, M., Nenes, A., Bauer, S. E., Bergman, T., Carslaw, K. S., Grini, A., 387

Hamilton, D. S., Johnson, J. S., Karydis, V. A., Kirkevåg, A., Kodros, J. K., Lohmann, U., Luo, G., 388

Makkonen, R., Matsui, H., Neubauer, D., Pierce, J. R., Schmale, J., Stier, P., Tsigaridis, K., van Noije, 389

T., Wang, H., Watson-Parris, D., Westervelt, D. M., Yang, Y., Yoshioka, M., Daskalakis, N., Decesari, 390

S., Gysel-Beer, M., Kalivitis, N., Liu, X., Mahowald, N. M., Myriokefalitakis, S., Schrödner, R., 391

Sfakianaki, M., Tsimpidi, A. P., Wu, M., and Yu, F.: Evaluation of global simulations of aerosol 392


13

particle and cloud condensation nuclei number, with implications for cloud droplet formation, Atmos. 393

Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, 2019. 394

Fry, J. L., Brown, S. S., Middlebrook, A. M., Edwards, P. M., Campuzano-Jost, P., Day, D. A., Jimenez, 395

J. L., Allen, H. M., Ryerson, T. B., Pollack, I., Graus, M., Warneke, C., de Gouw, J. A., Brock, C. A., 396

Gilman, J., Lerner, B. M., Dubé, W. P., Liao, J., and Welti, A.: Secondary organic aerosol (SOA) 397

yields from NO3 radical + isoprene based on nighttime aircraft power plant plume transects, Atmos. 398

Chem. Phys., 18, 11663-11682, https://doi.org/10.5194/acp-18-11663-2018, 2018. 399

Cerully, K. M., Bougiatioti, A., Hite, Jr., J. R., Guo, H., Xu, L., Ng, N. L., Weber, R., and Nenes, A.: On 400

the link between hygroscopicity, volatility, and oxidation state of ambient and water-soluble aerosols 401

in the southeastern United States, Atmospheric Chemistry and Physics, 15, 8679–8694, 2015. 402

IPCC (Intergovernmental Panel on Climate Change): Climate Change 2013: The Physical Science Basis, 403

Summary for Policymakers, Cambridge University Press, Cambridge, UK and New York, 2013. 404

Kacarab, M., Thornhill, K. L., Dobracki, A., Howell, S. G., O'Brien, J. R., Freitag, S., Poellot, M. R., Wood, 405

R., Zuidema, P., Redemann, J., and Nenes, A.: Biomass burning aerosol as a modulator of the droplet 406

number in the southeast Atlantic region, Atmos. Chem. Phys., 20, 3029–3040, 407

https://doi.org/10.5194/acp-20-3029-2020, 2020. 408

Kalkavouras, P., Bossioli, E., Bezantakos, S., Bougiatioti, A., Kalivitis, N., Stavroulas, I., Kouvarakis, G., 409

Protonotariou, A. P., Dandou, A., Biskos, G., Mihalopoulos, N., Nenes, A., and Tombrou, M.: New 410

particle formation in the southern Aegean Sea during the Etesians: importance for CCN production 411

and cloud droplet number, Atmos. Chem. Phys., 17, 175-192, https://doi.org/10.5194/acp-17-175-412

2017, 2017. 413

Kalkavouras, P., Bougiatioti, A., Kalivitis, N., Stavroulas, I., Tombrou, M., Nenes, A., and Mihalopoulos, 414

N.: Regional new particle formation as modulators of cloud condensation nuclei and cloud droplet 415

number in the eastern Mediterranean, Atmos. Chem. Phys., 19, 6185–6203, 416

https://doi.org/10.5194/acp-19-6185-2019, 2019. 417

Kupc, A., Williamson, C., Wagner, N. L., Richardson, M., and Brock, C. A.: Modification, calibration, and 418

performance of the Ultra-High Sensitivity Aerosol Spectrometer for particle size distribution and 419

volatility measurements during the Atmospheric Tomography Mission (ATom) airborne campaign, 420

Atmos. Meas. Tech., 11, 369–383, https://doi.org/10.5194/amt-11-369-2018, 2018. 421

Lathem, T. L., Beyersdorf, A. J., Thornhill, K. L., Winstead, E. L., Cubison, M. J., Hecobian, A., Jimenez, 422

J. L., Weber, R. J., Anderson, B. E., and Nenes, A.: Analysis of CCN activity of Arctic aerosol and 423

Canadian biomass burning during summer 2008, Atmos. Chem. Phys., 13, 2735-2756, 424

https://doi.org/10.5194/acp-13-2735-2013, 2013. 425


14

Leibensperger, E. M., Mickley, L. J., Jacob, D. J., Chen, W.-T., Seinfeld, J. H., Nenes, A., Adams, P. J., 426

Streets, D. G., Kumar, N., and Rind, D.: Climatic effects of 1950–2050 changes in US anthropogenic 427

aerosols – Part 1: Aerosol trends and radiative forcing, Atmos. Chem. Phys., 12, 3333-3348, 428

https://doi.org/10.5194/acp-12-3333-2012, 2012a. 429

Leibensperger, E. M., Mickley, L. J., Jacob, D. J., Chen, W.-T., Seinfeld, J. H., Nenes, A., Adams, P. J., 430

Streets, D. G., Kumar, N., and Rind, D.: Climatic effects of 1950–2050 changes in US anthropogenic 431

aerosols – Part 2: Climate response, Atmos. Chem. Phys., 12, 3349-3362, https://doi.org/10.5194/acp-432

12-3349-2012, 2012b. 433

Middlebrook, A. M., Bahreini, R., Jimenez, J. L., and Canagaratna, M. R.: Evaluation of Composition-434

Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data, 435

Aerosol Sci. Tech., 46, 258–271, doi.org/10.1080/02786826.2011.620041, 2012. 436

Moore, R. H., Bahreini, R., Brock, C. A., Froyd, K. D., Cozic, J., Holloway, J. S., Middlebrook, A. M., 437

Murphy, D. M., and Nenes, A.: Hygroscopicity and composition of Alaskan Arctic CCN during April 438

2008, Atmos. Chem. Phys., 11, 11 807–11 825, 2011. 439

Moore, R. H., Cerully, K., Bahreini, R., Brock, C. A., Middlebrook, A. M., and Nenes, A.: Hygroscopicity 440

and composition of California CCN during summer 2010, J. Geophys. Res., 117, 2012. 441

Morales, R. and Nenes, A.: Characteristic updrafts for computing distribution-averaged cloud droplet 442

number, autoconversion rate effective radius, J. Geophys. Res., 115, D18220, 443

doi:10.1029/2009JD013233, 2010. 444

Morales Betancourt, R. and Nenes, A.: Droplet activation parameterization: the population-splitting 445

concept revisited, Geosci. Model Dev., 7, 2345–2357, https://doi.org/10.5194/gmd-7-2345-2014, 446

2014a. 447

Morales Betancourt, R. and Nenes, A.: Understanding the contributions of aerosol properties and 448

parameterization discrepancies to droplet number variability in a global climate model, Atmos. Chem. 449

Phys., 14, 4809-4826, https://doi.org/10.5194/acp-14-4809-2014, 2014b. 450

Nenes, A. and Seinfeld, J.H.: Parameterization of cloud droplet formation in global climate models, J. 451

Geophys. Res, 108, 4415, doi:10.1029/2002JD002911, 2003. 452

Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud 453

condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, doi:10.5194/acp-7-1961-2007, 454

2007. 455

Quinn, P. K., Bates, T. S., Coffman, D. J., and Covert, D. S.: Influence of particle size and chemistry on 456

the cloud nucleating properties of aerosols, Atmos. Chem. Phys., 8, 1029-1042, 457

https://doi.org/10.5194/acp-8-1029-2008, 2008. 458


15

Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe, S. S., Wernli, H., Andreae, M. O., and 459

Pöschl, U.: Aerosol- and updraft-limited regimes of cloud droplet formation: influence of particle 460

number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN), Atmos. Chem. 461

Phys., 9, 7067-7080, https://doi.org/10.5194/acp-9-7067-2009, 2009. 462

Seidel, D. J., Zhang, Y., Beljaars, A., Golaz, J.-C., Jacobson, A. R., and Medeiros, B.: Climatology of the 463

planetary boundary layer over the continental United States and Europe, J. Geophys. Res., 117, 464

D17106, doi:10.1029/2012JD018143, 2013. 465

Seinfeld, J., and Pandis, S.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 466

John Wiley, Hoboken, N. J., 1998. 467

Seinfeld, J. H., Bretherton, C. S., Carslaw, K. S., Coe, H., De- Mott, P. J., Dunlea, E. J., Feingold, G., Ghan, 468

S. J., Guenther, A.B., Kahn, R. A., Kracunas, I. P., Kreidenweis, S. M., Molina, M. J., Nenes, A., 469

Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., 470

Rosenfeld, D., Stephens, G., and Wood R.: Improving Our Fundamental Understanding of the Role of 471

Aerosol-Cloud Interactions in the Climate System, P. Nat. Acad. Sci. USA, 113, 5781–5790, 472

https://doi.org/10.1073/pnas.1514043113, 2016. 473

Sullivan, S.C., Lee, D., Oreopoulos, L., and Nenes, A.: The role of updraft velocity in temporal variability 474

of cloud hydrometeor number, Proc.Nat.Acad.Sci, 113, 21, doi: 10.1073/pnas.1514039113, 2016. 475

Wagner, N. L., Brock, C. A., Angevine, W. M., Beyersdorf, A., Campuzano-Jost, P., Day, D., de Gouw, J. 476

A., Diskin, G. S., Gordon, T. D., Graus, M. G., Holloway, J. S., Huey, G., Jimenez, J. L., Lack, D. A., 477

Liao, J., Liu, X., Markovic, M. Z., Middlebrook, A. M., Mikoviny, T., Peischl, J., Perring, A. E., 478

Richardson, M. S., Ryerson, T. B., Schwarz, J. P., Warneke, C., Welti, A., Wisthaler, A., Ziemba, L. 479

D., and Murphy, D. M.: In situ vertical profiles of aerosol extinction, mass, and composition over the 480

southeast United States during SENEX and SEAC4RS: observations of a modest aerosol enhancement 481

aloft, Atmos. Chem. Phys., 15, 7085-7102, https://doi.org/10.5194/acp-15-7085-2015, 2015. 482

Warneke, C., Trainer, M., de Gouw, J. A., Parrish, D. D., Fahey, D. W., Ravishankara, A. R., Middlebrook, 483

A. M., Brock, C. A., Roberts, J. M., Brown, S. S., Neuman, J. A., Lerner, B. M., Lack, D., Law, D., 484

Hübler, G., Pollack, I., Sjostedt, S., Ryerson, T. B., Gilman, J. B., Liao, J., Holloway, J., Peischl, J., 485

Nowak, J. B., Aikin, K. C., Min, K.-E., Washenfelder, R. A., Graus, M. G., Richardson, M., Markovic, 486

M. Z., Wagner, N. L., Welti, A., Veres, P. R., Edwards, P., Schwarz, J. P., Gordon, T., Dube, W. P., 487

McKeen, S. A., Brioude, J., Ahmadov, R., Bougiatioti, A., Lin, J. J., Nenes, A., Wolfe, G. M., Hanisco, 488

T. F., Lee, B. H., Lopez-Hilfiker, F. D., Thornton, J. A., Keutsch, F. N., Kaiser, J., Mao, J., and Hatch, 489

C. D.: Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of 490

the Southeast Atmosphere Study 2013, Atmos. Meas. Tech., 9, 3063-3093, 491

https://doi.org/10.5194/amt-9-3063-2016, 2016. 492


16

Wilson, J. C., Lafleur, B. G., Hilbert, H., Seebaugh, W. R., Fox, J., Gesler, D. W., Brock, C. A., Huebert, 493

B. J., and Mullen, J.: Function and performance of a low turbulence inlet for sampling supermicron 494

particles from aircraft platforms, Aerosol Sci. Tech., 38, 790–802, 2004. 495

Weber RJ, Guo H, Russell AG, Nenes A.: High aerosol acidity despite declining atmospheric sulfate 496

concentrations over the past 15 years, Nat. Geosci., 9, 282-285, 2016. 497

Yu, S., Alapaty, K., Mathur, R., Pleim, J., Zhang, Y., Nolte, C., Eder, B., Foley, K., and Nagashima, T.: 498

Attribution of the United States “warming hole”: Aerosol indirect effect and precipitable water vapor. 499

Sci. Rep., 4, 6929, https:// doi.org/10.1038/srep06929, 2014. 500


17

Table 1: Research flights from SENEX 2013 used in this study. 501

Flight Date Local Time

(CDT, UTC-5 hrs)

κ Organic mass

fraction

4 10/6 09:55-16:30 0.23±0.02 0.62±0.11

5 11/6 11:30-17:57 0.20±0.00 0.68±0.05

6 12/6 09:48-15:31 0.21±0.01 0.68±0.07

9 19/6 17:30-23:29 0.24±0.01 0.66±0.06

10 22/6 10:01-17:09 0.21±0.02 0.68±0.08

11 23/6 10:08-17:22 0.25±0.03 0.58±0.07

12 25/6 10:18-17:25 0.39±0.02 0.35±0.18

14 29/6 10:26-17:39 0.22±0.03 0.62±0.07

15 2/7 20:08-02:51 0.28±0.05 0.55±0.09

16 3/7 19:56-02:55 0.22±0.05 0.67±0.09

17 5/7 09:52-16:24 0.23±0.05 0.59±0.14

18 6/7 09:19-16:18 0.31±0.02 0.52±0.08

19 8/7 10:11-16:44 0.23±0.04 0.62±0.08

502

503

504


18

Table 2: Flight number, time interval, spectral dispersion of vertical wind velocity (σw) and characteristic 505 vertical velocity w*=0.79σw during flight segments where the aircraft flew at a constant altitude. 506

Flight

(pass)

Time

Range σw

(m s-1)

w*

(m s-1)

Altitude (m) Flight

(pass)

Time

Range

σw

(m s-1)

w*

(m s-1)

Altitude (m)

5 (1) 12:31-12:58 1.02 0.81 549± 58 9 (1) 18:44-18:58 0.255 0.202 797±2.01

5 (2) 13:16-13:29 0.82 0.65 982±11 9 (2) 19:20-19:29 0.249 0.197 740±1.23

5 (3) 13:34-13:50 1.01 0.80 502±13 9 (3) 19:33-19:48 0.217 0.171 740±1.23

5 (4) 13:53-14:08 1.03 0.81 614±27 9 (4) 19:51-20:25 0.218 0.173 776±1.22

5 (5) 14:20-15:00 0.91 0.72 603±40 9 (5) 20:34-20:39 0.232 0.183 597±1.19

5 (6) 15:35-15:41 0.87 0.69 533±18 9 (7) 20:56-21:10 0.201 0.158 773±1.11

5 (7) 16:17-16:30 0.77 0.61 638±23 9 (8) 21:31-21:45 0.191 0.151 725±1.18

5 (8) 16:31-16:39 0.55 0.44 559±18 9 (9) 22:24-22:31 0.257 0.203 745± 1.36

5 (9) 17:10-17:22 0.53 0.42 686±40 9 (10) 22:48-22:54 0.221 0.175 804± 1.37

14 (1) 12:34-12:49 0.94 0.75 558±2 15 (1) 21:09-21:52 0.236 0.186 505±6.64

14 (2) 13:57-14:17 0.97 0.77 658±3 15 (2) 22:19-22:31 0.301 0.238 633±1.21

14 (3) 14:22-14:46 0.95 0.75 737±3 15 (3) 22:42-22:54 0.255 0.202 600±1.17

14 (4) 14:58-15:33 0.55 0.43 746±23 15 (4) 23:26-23:37 0.329 0.260 908±1.56

14 (5) 15:55-16:08 0.57 0.45 714±3 15 (5) 00:02-00:19 0.297 0.235 1208±1.23

14 (6) 16:11-16:21 0.77 0.61 801±3 15 (6) 00:43-1:08 0.253 0.199 592±1.37

14 (7) 16:33-16:41 0.45 0.35 793± 2 15 (7) 1:10-1:24 0.276 0.218 676±1.02

15 (8) 1:37-2:02 0.207 0.164 713±19.5

12 (1) 11:50-12:34 0.96 0.75 484±3 19 (1) 11:20-11:41 0.622 0.492 1014±2.27

12 (2) 12:48-13:18 1.09 0.86 503±3 19 (2) 12:09-12:23 1.203 0.95 652±3.34

12 (3) 13:34-13:50 1.12 0.88 894±3 19 (3) 12:51-13:10 0.873 0.689 537±2.51

12 (4) 14:06-14:40 1.04 0.82 479±4 19 (4) 13:22-13:49 1.294 1.022 518±22.6

12 (5) 15:21-15:32 1.10 0.87 521±3 19 (5) 14:44-14:57 1.361 1.075 528±3.26

12 (6) 15:43-16:02 0.99 0.78 475±3 19 (6) 15:04-16:06 0.896 0.708 524±2.8

507

508


19

Table 3: Derived cloud parameters (maximum supersaturation, droplet number) and relative contribution of chemical composition and total 509 aerosol number for different vertical velocities. Numbers in parentheses indicate standard deviation values. 510

511

Flight Na Na

variab

σw=0.1 m s-1 σw=0.3 m s-1 σw=0.6 m s-1 σw=1.0 m s-1

Smax Nd Cont

κ

Cont

Νa

Smax Nd Cont

κ

Cont

Νa

Smax Nd Cont

κ

Cont

Νa

Smax Nd Cont

κ

Cont

Νa

4 6118 4520 0.11

(0.06)

122

(41)

0.08 0.92 0.16

(0.09)

315

(114)

0.20 0.80 0.21

(0.12)

520

(212)

0.23 0.77 0.26

(0.17)

737

(321)

0.2 0.8

5 4324 2598 0.08

(0.04)

139

(31)

0.09 0.91 0.1

(0.06)

388

(104)

0.15 0.85 0.14

(0.08)

712

(216)

0.17 0.83 0.17

(0.1)

1063

(360)

0.21 0.79

6 4958

3054 0.07

(0.07)

151

(24)

0.03 0.97 0.08

(0.04)

422

(70)

0.11 0.89 0.1

(0.06)

773

(171)

0.08

0.92 0.13

(0.07)

1162

(302)

0.07 0.93

9 4271 3095 0.07

(0.02)

152

(18)

0.05 0.95 0.12

(0.04)

367

(68)

0.17 0.83 0.16

(0.05)

533

(115)

0.17 0.83 0.19

(0.06)

680

(126)

0.12 0.88

10 6286 7201 0.07

(0.03)

158

(24)

0.02 0.98 0.1

(0.05)

422

(86)

0.02 0.98 0.14

(0.07)

748

(180)

0.04 0.96 0.18

(0.08)

1063

(295)

0.09 0.91

11 5969 7271 0.04

(0.01)

137

(19)

0.01 0.99 0.06

(0.01)

381

(61)

0.04 0.96 0.08

(0.02)

695

(134)

0.03

0.97 0.10

(0.02)

1025

(226)

0.03 0.97

12 3154 5150 0.06

(0.03)

110

(45)

0.03 0.97 0.1

(0.04)

274

(117)

0.05 0.95 0.14

(0.04)

404

(179)

0.08 0.92 0.17

(0.05)

486

(207)

0.07 0.93

14 5564 5891 0.07

(0.02)

118

(41)

0.05 0.95 0.10

(0.03)

328

(125)

0.17 0.83 0.13

(0.04)

590

(240)

0.25 0.75 0.16

(0.05)

842

(361)

0.27 0.73

15 2328 1428 0.05

(0.01)

135

(22)

0.03 0.97 0.09

(0.02)

339

(67)

0.12 0.88 0.12

(0.02)

557

(137)

0.21 0.79 0.16

(0.03)

717

(203)

0.3

0.7

16 3440 4507 0.08

(0.06)

158

(37)

0.03 0.97 0.12

(0.1)

403

(120)

0.06 0.94 0.17

(0.13)

670

(235)

0.07 0.93 0.23

(0.16)

917

(374)

0.1 0.9

17 3813 4645 0.05

(0.02)

129

(41)

0.06 0.94 0.07

(0.03)

342

(130)

0.1 0.9 0.1

(0.04)

593

(248)

0.06 0.94 0.13

(0.05)

841

(371)

0.06

0.94

18 1925 983 0.08

(0.04)

90

(58)

0.12 0.88 0.12

(0.05)

233

(157)

0.35 0.65 0.15

(0.06)

379

(262)

0.37 0.63 0.19

(0.07)

499

(346)

0.27

0.73

19 4323 7261 0.06

(0.02)

121

(33)

0.02 0.98 0.08

(0.02)

314

(96)

0.06 0.94 0.12

(0.03)

526

(177)

0.11 0.89 0.15

(0.03)

670

(249)

0.13 0.87

512

513


20

Table 4: Derived Smax, Nd, σw for all research flights along with the estimated contribution of each 514 parameter to the variability of the droplet number. 515

Flight σw

(m s-1)

𝚫𝝈𝒘𝝈𝒘

Smax

(%)

Nd

(cm-3)

𝚫𝑵𝒅𝑵𝒅

Contrib.

κ

Contrib.

Na

Contrib.

σw

4 1.03±0.25 0.243 0.29±0.19 707±343 0.485 4% 79% 17%

5 0.97±0.1 0.103 0.17±0.10 1040±350 0.337 7% 69% 24%

6 0.94±0.18 0.191 0.13±0.07 1108±283 0.255 3% 54% 43%

9 0.23±0.02 0.043 0.10±0.03 309±51 0.165 7% 76% 17%

10 1.22±0.11 0.090 0.12±0.03 1177±271 0.230 1% 90% 9%

11 1.08±0.04 0.037 0.11±0.03 1082±242 0.224 1% 83% 16%

12 1.05±0.07 0.067 0.18±0.05 495±210 0.424 2% 96% 2%

14 0.85±0.2 0.024 0.15±0.04 761±321 0.422 9% 72% 19%

15 0.28±0.04 0.143 0.08±0.02 321±63 0.196 7% 51% 42%

16 0.20±0.04 0.200 0.10±0.08 289±79 0.273 2% 65% 33%

17 0.71±0.26 0.366 0.15±0.11 742±280 0.377 1% 71% 28%

18 0.90±0.06 0.067 0.31±0.18 538±325 0.604 7% 83% 10%

19 0.99±0.31 0.313 0.15±0.03 699±248 0.355 4% 88% 8%

516


21

(a) (b)

Figure 1: Spatial and vertical distribution of the organics mass fraction (a) for Flight 5 and (b) for Flight 517 12, denoting the difference in chemical composition, which in turn, may influence cloud droplet number 518 concentration. The color scale denotes the percentage of the organics mass fraction. 519

520


22

521

Figure 2: Average particle number size distributions for: (a) free tropospheric conditions, (b) within the 522 boundary layer, (c) for flights with high variability in total aerosol number, and (d) during nighttime flights. 523 Error bars represent the 75th percentile of the distributions within each pass. 524

525

14x103

12

10

8

6

4

2

0

dN

/dlo

gD

p (cm

-3)

4 5 6

102 3 4 5 6

1002 3 4 5 6

10002 3

Dp (nm)

Flight5 pass2 Flight11 pass6 Flight18 pass1 Fligh16 pass3

20x103

15

10

5

0

dN

/dlo

gD

p (

cm

-3)

4 5 6

102 3 4 5 6

1002 3 4 5 6

10002 3

Dp (nm)

Flight5 pass1 Flight6 pass2 Flight10 pass7 Flight14 pass1

25x103

20

15

10

5

0

dN

/dlo

gD

p (

cm

-3)

4 5 6

102 3 4 5 6

1002 3 4 5 6

10002 3

Dp (nm)

Flight4 pass2 Flight10 pass3 Flight11 pass1 Flight14 pass6

6000

5000

4000

3000

2000

1000

0

dN

/dlo

gD

p (cm

-3)

4 5 6

102 3 4 5 6

1002 3 4 5 6

10002 3

Dp (nm)

Flight16 pass1 Flight15 pass2 Flight9 pass4

(a) (b)

(c) (d)


23

(a)

(b)

(c)

(d)

526

Figure 3: Flight trajectories showing cloud droplet number (indicated by marker size (cm-3)) and total 527 aerosol number (indicated by marker color) for the observed characteristic vertical velocity (w*). (a) for the 528 Alabama sector during daytime (Flight 5) and (b) nighttime (Flight 15). (c) for Atlanta during daytime 529 (Flight 6) and (d) nighttime (Flight 9). Note that the data are plotted at less than 1 Hz in order to better show 530 the size and color of the markers. 531

532

533

534

535

536


24

(a)

(b)

(c)

(d)

537

Figure 4: Cloud droplet number vs. total aerosol number for the derived characteristic vertical velocity 538 (w*). (a) for the Alabama sector during daytime (Flight 5) and (b) nighttime (Flight 15). (c) for Atlanta 539 during daytime (Flight 6) and (d) nighttime (Flight 9). Data are colored by maximum supersaturation. 540

541

542

543

1400

1200

1000

800

600

400

200

Dro

ple

t num

ber

20x103151050

Naerosol (cm-3

)

0.50.40.30.20.1

Smax (%)

400

350

300

250

200

150

100

Dro

ple

t num

ber

1000080006000400020000

Naerosol (cm-3

)

0.50.40.30.20.1

Smax (%)

1400

1200

1000

800

600

400

200

Dro

ple

t num

ber

20x103151050

Naerosol (cm-3

)

0.50.40.30.20.1

Smax (%)

400

350

300

250

200

150

Dro

ple

t n

um

be

r

20x103151050

Naerosol (cm-3

)

0.50.40.30.20.1

Smax (%)


25

544

Figure 5: Average cloud droplet number vs. characteristic velocity during the 13 research flights, colored 545 by total aerosol number. Error bars represent the standard deviation of cloud droplet number during each 546 flight. The shaded area represents the flights conducted during nighttime. 547

548

549

1600

1400

1200

1000

800

600

400

200

0

Dro

ple

t n

um

ber

Nd (

cm

-3)

1.00.80.60.40.2

w* (m s

-1)

Flight 17

Nighttime Flights 9, 15 & 16

Flights 5 & 6

Flight 18Flight 12

Flight 11

Flight 10

Flights 4 & 19

Flight 14

60005000400030002000

Naerosol (cm-3

)


26

550

Figure 6: Total aerosol number vs. characteristic velocity during the segments of the flights when the 551 aircraft remained at a constant altitude within the boundary layer, colored by relative humidity. The shaded 552 area represents the segments of the flights conducted during nighttime. 553

554


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